Abstract

Information Retrieval (IR) is a profound technique to find information that addresses the need of query. Processing of normal text is easier and information can be retrieved efficiently. There are plenty of algorithms in hand to carry out the normal text retrieval. Whereas retrieving geospatial information is very complex and requires additional operations to be performed. Since geospatial data contain complex details than general data such as location, direction. To handle geographical queries, we proposed a Density Probabilistic Document Correlation (DPDC) approach. This approach, initially categorize the geographical features from text that satisfies the given queries. Existing text classification techniques are unsuitable for geospatial text classification due to the exclusivity of the geographical features. Depending on the DPDC approach result we predict overlap of the feature set for a document. Based on overlap and document correlation, the documents are ranked. Highly relevant documents are extracted depending on the score obtained through ranking. Documents with high score are considered the most relevant. The experimental results show that our proposed method efficiently retrieves the list of relevant documents.